Computer Science Department, Stevens Institute of Technology, Hoboken, NJ, USA.
Department of Psychology, Lehigh University, Bethlehem, PA, USA.
Cogn Res Princ Implic. 2023 Aug 30;8(1):57. doi: 10.1186/s41235-023-00509-7.
Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them successfully. We investigate the trade-off between simplicity and complexity in decision making, testing diagrams tailored to target choices (Experiments 1 and 2), and with relevant causal paths highlighted (Experiment 3), finding that simplicity or directing attention to simple causal paths leads to better decisions. We test the boundaries of this effect (Experiment 4), finding that including a small amount of information beyond that related to the target answer has a detrimental effect. Finally, we examine whether people know what information they need (Experiment 5). We find that simple, targeted, information still leads to the best decisions, while participants who believe they do not need information or seek out the most complex information performed worse.
人们每天都会在健康和个人财务等复杂主题上做出决策。已经创建了这些领域的因果模型来辅助决策，但得到的模型往往很复杂，并且不知道人们是否能够成功地使用它们。我们研究了决策中的简单性和复杂性之间的权衡，测试了针对目标选择的图表（实验 1 和 2），并突出了相关的因果路径（实验 3），发现简单性或将注意力集中在简单的因果路径上会导致更好的决策。我们测试了这种效果的边界（实验 4），发现包含与目标答案相关的少量信息以外的信息会产生不利影响。最后，我们检查了人们是否知道他们需要什么信息（实验 5）。我们发现，简单、有针对性的信息仍然可以做出最佳决策，而那些认为自己不需要信息或寻求最复杂信息的参与者表现更差。
